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Intracellular spatial transcriptomic analysis toolkit (InSTAnT)

Author

Listed:
  • Anurendra Kumar

    (Georgia Institute of Technology)

  • Alex W. Schrader

    (University of Illinois Urbana-Champaign)

  • Bhavay Aggarwal

    (Georgia Institute of Technology)

  • Ali Ebrahimpour Boroojeny

    (University of Illinois Urbana-Champaign)

  • Marisa Asadian

    (University of Illinois Urbana-Champaign)

  • JuYeon Lee

    (University of Illinois Urbana-Champaign)

  • You Jin Song

    (University of Illinois Urbana-Champaign)

  • Sihai Dave Zhao

    (University of Illinois Urbana-Champaign
    University of Illinois Urbana-Champaign)

  • Hee-Sun Han

    (University of Illinois Urbana-Champaign
    University of Illinois Urbana-Champaign)

  • Saurabh Sinha

    (Georgia Institute of Technology
    Georgia Institute of Technology)

Abstract

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

Suggested Citation

  • Anurendra Kumar & Alex W. Schrader & Bhavay Aggarwal & Ali Ebrahimpour Boroojeny & Marisa Asadian & JuYeon Lee & You Jin Song & Sihai Dave Zhao & Hee-Sun Han & Saurabh Sinha, 2024. "Intracellular spatial transcriptomic analysis toolkit (InSTAnT)," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49457-w
    DOI: 10.1038/s41467-024-49457-w
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    References listed on IDEAS

    as
    1. Anjali Rao & Dalia Barkley & Gustavo S. França & Itai Yanai, 2021. "Exploring tissue architecture using spatial transcriptomics," Nature, Nature, vol. 596(7871), pages 211-220, August.
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